GHG Inventory hands-on trainingWorkshop of the CGE

Ricardo Leonardo Vianna Rodrigues

Difficulties in calculating net CO2 emissions from Brazilian

agricultural soils

Panama, October 2004

IPCC: three potential sources of CO2 emissions from soils

Net carbon stock changes from mineral soils associated with land use change and management;

Emissions from liming of agricultural soils;

Emissions from cultivated organic soils;

Net carbon stock changes from mineral soils

associated with land use change and management

DATA NEEDED

Land use area (grassland, grain etc) in the year t and in the year t-20, for each soil type,

Soil carbon content from different soil types (top 30 cm depth),

Types of land management and impact factors

DATA NEEDED

Land use area (grassland, grain etc) in the year t and in the year t-20, for each soil type, Agriculture Census Remote sensing data

Agriculture Census

ADVANTAGES Available (do not

requires much effort to be collected)

Systematically collected every 5 or 10 years,

Land use type data,

DISADVANTAGES Do not cover all

land area, Considers only

data of properties economically active

Data are non geo-referenced,

Lack of management data

Remote sensing data

ADVANTAGES Data are geo-

referenced, Land use data

may be collected, Multi-temporal

data allows estimates of deforestation rate,

DISADVANTAGES Very expensive

data Land use data not

available (have to be collected),

Need specialists to analyze RS data,

Other management data are unlikely to be collected,

Activity data choice

Experts in net flux of CO2 from soils chose Brazilian Agricultural Census as the most suitable data;

Difficulties: Brazilian Agricultural Census

considers only rural properties that are economically active;

Besides, Census do not take into account agriculture management,

Agricultural Census Data

Consequences of excluding rural properties that are economically inactive from Census:

Deforestation rate may have been underestimated in some regions,

Because of that, changes in soil carbon stock may be underestimated;

Consequences of using non geo-referenced data

When data are non geo-referenced, as happens with Agriculture Census, the integration between soil C content and land use is hindered;

Alternative: to assume that each soil class have the same proportion of land use classes for a determined region (high uncertainties);

DATA NEEDED

Land use area (grassland, grain etc) in the year t and in the year t-20, for each soil type,

Soil carbon content from different soil types (top 30 cm depth),

Types of land management and impact factors

Carbon Soil Data

Native soil carbon content from different soil types (top 30 cm depth) Soil survey data from different sources Phyto-physiognomic maps Soil distribution maps

Difficulties associated to soil carbon content data

The soil data base comes from different sources and scales (generally, large scale);

C content was estimated by different methods, and unsuitable methods, increasing uncertainties,

Carbon stock = Bulk Density * Carbon * horizon thickness (top 30 cm)

Lack of bulk density data in most of soil profiles (g dry soil/cm3 - which includes the pore spaces); and the solution was to use of multiple linear regression equations to estimate bulk density;

DATA NEEDED

Land use area (grassland, grain etc) in the year t and in the year t-20, for each soil type,

Soil carbon content from different soil types (top 30 cm depth),

Types of land management and impact factors

Types of land management and impact factors

Lack of specific data for Brazil (residue addition levels, tillage systems, pasture conditions) may have hindered estimations;

Lack of Brazilian impact factors – use of IPCC coefficients and EF default for tropical regions, which may not be representative of Brazilian conditions;

Emissions from liming of agricultural soils

Emissions from liming of

agricultural soils

• Lack of suitable statistics about amount of agricultural lime sold yearly in Brazil;

• Data were obtained from the greatest Lime Producers Associations;

• Lack of detail about the composition of lime sold in Brazil;

Emissions from cultivated organic soils

Emissions from cultivated organic

soils

Lack of cultivated organic soils data;

Use as proxy agriculture data usually associated to this kind of soil.

Conclusions

Estimations of net CO2 emissions from Brazilian agricultural soils may have been

hindered by

Lack of suitable land use and management data;

Different and unsuitable methods to estimate soil carbon content;

Lack of suitable impact factors, Data are non geo-referenced; Lack of suitable lime production

statistics; Lack of cultivated organic soils statistics;

Steps Forward

Next Inventory In the next Brazilian inventory is

likely that remote sensing data will be used for areas under high deforestation rate whereas Agricultural Census will be used for rural areas already consolidated.